Identification and prioritization of optimum capacity solutions in a telecommunications network

ABSTRACT

Systems and methods that use historical data comprising capacity gain solutions and their associated gains at various locations to train a machine learning model. The trained machine learning model, upon receiving a new location (e.g., latitude and longitude coordinates), recommends the top n (e.g., the top 3) solutions that should be deployed at the new location to improve telecommunications network performance. The machine learning model uses clustering techniques to perform the recommendations.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a division of U.S. patent application Ser. No.16/855,991, filed Apr. 22, 2020, entitled IDENTIFICATION ANDPRIORITIZATION OF OPTIMUM CAPACITY SOLUTIONS IN A TELECOMMUNICATIONSNETWORK, which is hereby incorporated by reference in its entirety.

BACKGROUND

A telecommunications network is established via a complex arrangementand configuration of many cell sites that are deployed across ageographical area. For example, there can be different types of cellsites (e.g., macro cells, microcells, and so on) positioned in aspecific geographical location, such as a city, neighborhood, and soon). These cell sites strive to provide adequate, reliable coverage formobile devices (e.g., smart phones, tablets, and so on) via differentfrequency bands and radio networks such as a Global System for Mobile(GSM) mobile communications network, a code/time division multipleaccess (CDMA/TDMA) mobile communications network, a 3rd or 4thgeneration (3G/4G) mobile communications network (e.g., General PacketRadio Service (GPRS/EGPRS)), Enhanced Data rates for GSM Evolution(EDGE), Universal Mobile Telecommunications System (UMTS), or Long TermEvolution (LTE) network), 5G mobile communications network, IEEE 802.11(WiFi), or other communications networks. The devices can seek access tothe telecommunications network for various services provided by thenetwork, such as services that facilitate the transmission of data overthe network and/or provide content to the devices.

As device usage continues to rise at an impressive rate, there are toomany people using too many network (and/or data)-hungry applications inplaces where the wireless edge of the telecommunications network haslimited or no capacity. As a result, most telecommunications networkshave to contend with issues of network congestion. Network congestion isthe reduced quality of service that occurs when a network node carriesmore data than it can handle. Typical effects include queueing delay,packet loss or the blocking of new connections, overall resulting indegraded customer experience. brief description of the drawings.

When performance of a cell site in a telecommunications network degradesbelow a threshold value (for example, an LTE site gets congested),different solutions have been suggested to address and resolve thedegradation issues. However, it is difficult for wirelesstelecommunication service providers to determine which solution would beeffective, optimal and cost-effective for the degraded site.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram illustrating a suitable computing environmentwithin which to identify optimum network performance improvementsolutions within a telecommunications network.

FIG. 2 is a block diagram illustrating the components of the optimumcapacity solution system.

FIGS. 3A-3F are example of data accessed/received/collected by thehistorical data module.

FIGS. 4A-4B illustrate scores generated for various sites in ageographic area.

FIGS. 5A-5B are flow diagrams illustrating a process of identifyingoptimum network performance improvement solution at a location in atelecommunications network.

FIGS. 6A-6B illustrate examples of generating clusters for a set ofnetwork improvement solutions records associated with a geographic area.

In the drawings, some components and/or operations can be separated intodifferent blocks or combined into a single block for discussion of someof the implementations of the present technology. Moreover, while thetechnology is amenable to various modifications and alternative forms,specific implementations have been shown by way of example in thedrawings and are described in detail below. The intention, however, isnot to limit the technology to the specific implementations described.On the contrary, the technology is intended to cover all modifications,equivalents, and alternatives falling within the scope of the technologyas defined by the appended claims.

DETAILED DESCRIPTION

An aim of a telecommunications service provider is to minimize customerexperience degradation. This is typically achieved by deployingcongestion management and/or network improvement solutions at one ormore cell sites. To combat network congestion, different capacityplanning solutions have been suggested to address and resolve thedegradation issues. However, since a wide variety of capacity planningsolutions are available as options to resolve degradation issues, it isdifficult to determine which solutions, if any, are the best candidatesto deploy at particular locations. As a result, the process foridentifying which capacity planning solutions to deploy to alleviatenetwork congestion and/or improve capacity is more of a trial and errorprocess. This results in inefficiencies as well as wasted costs astelecommunications service providers try (and fail) deployingsub-optimum capacity planning solutions that are not tailored to theparticular location of network traffic usage and congestion.

To solve the above and other problems, the inventors have developed anoptimum capacity composite gain system and related method to identifyoptimum capacity planning solutions to improve telecommunicationsnetwork performance for a particular location (“optimum capacitysolution system”). One purpose of the optimum capacity solution systemis to summarize complex, multi-dimensional indicators to supportdecision making by wireless telecommunication service providers onchanges that may be needed to infrastructure repair, modification,planning and development. The optimum capacity solution system does thisby analyzing data related to capacity planning solutions deployed atspecific locations (e.g., historical data), learning from this data bycreating clusters, and applying classification techniques to determineoptimum capacity planning solutions capable of being deployed at a newlocation. As a result, a telecommunications service provider is able toefficiently and economically identify targeted solutions and locationsto expand capacity of cell sites and improve customer experiences.

The optimum capacity solution system uses historical data comprisingcapacity gain solutions and their associated gains at various locationsto train a machine learning model. The trained machine learning modelthen, upon receiving a new location (e.g., latitude and longitudecoordinates), recommends the top n (e.g., the top 3) solutions thatshould be deployed at the new location to improve telecommunicationsnetwork performance. The machine learning model uses clusteringtechniques to perform the recommendations. In some implementations, theoptimum capacity solution system builds/accesses a dataset comprisinginformation of previously deployed capacity improvement solutions, suchas gain, cost, location, duration, and solution type. It then performsclustering on the dataset per market for each solution to categorizesolutions within an area. Then, it ranks the solutions in each clusterbased on one or more of the following criterion: spectrum, duration,area (latitude/longitude), and cost. Once a customer inputs a newlocation (e.g., by entering latitude/longitude, clicking on a locationon a map, etc.), the optimum capacity solution system finds the nearestcluster and shows the top n solutions in the cluster. (While the term“customer” is used in the application, one of skill in the art willunderstand that the concepts discussed herein will similarly apply toother users, who may or may not be customers of a telecommunicationsservice provider.)

In the following description, for the purposes of explanation, numerousspecific details are set forth in order to provide a thoroughunderstanding of implementations of the present technology. It will beapparent, however, to one skilled in the art that implementations of thepresent technology can be practiced without some of these specificdetails.

The phrases “in some implementations,” “in several implementations,”“according to some implementations,” “in the implementations shown,” “inother implementations,” and the like generally mean the specificfeature, structure, or characteristic following the phrase is includedin at least one implementation of the present technology and can beincluded in more than one implementation. In addition, such phrases donot necessarily refer to the same implementations or differentimplementations.

Suitable Computing Environments

FIG. 1 is a block diagram illustrating a suitable computing environment100 within which to select optimum network performance improvementsolutions within a telecommunications network.

One or more user devices 110, such as mobile devices or user equipment(UE) associated with users (such as mobile phones (e.g., smartphones),tablet computers, laptops, and so on), Internet of Things (IoT) devices,devices with sensors, and so on, receive and transmit data, streamcontent, and/or perform other communications or receive services over atelecommunications network 130, which is accessed by the user device 110over one or more cell sites 120, 125. For example, the user device 110can access a telecommunication network 130 via a cell site at ageographical location that includes the cell site, in order to transmitand receive data (e.g., stream or upload multimedia content) fromvarious entities, such as a content provider 140, cloud data repository145, and/or other user devices 155 on the network 130 and via the cellsite 120.

The cell sites may include macro cell sites 120, such as base stations,small cell sites 125, such as picocells, microcells, or femtocells,and/or other network access component or sites (including IEEE 802.11WLAN access points). The cell cites 120, 125 can store data associatedwith their operations, including data associated with the number andtypes of connected users, data associated with the provision and/orutilization of a spectrum, radio band, frequency channel, and so on,provided by the cell sites 120, 125, and so on. The cell sites 120, 125can monitor their use, such as the provisioning or utilization of PRBsprovided by a cell site physical layer in LTE network. For example, acell site 120 having a channel bandwidth of 5 MHz that provides 25available physical resource blocks through which data can be transmittedto/from the user device 110.

Other components provided by the telecommunications network 130 canmonitor and/or measure the operations and transmission characteristicsof the cell sites 120, 125 and other network access components. Forexample, the telecommunications network 130 can provide a networkmonitoring system, via a network resource controller (NRC) or networkperformance and monitoring controller, or other network controlcomponent, in order to measure and/or obtain the data associated withthe utilization of cell sites 120, 125 when data is transmitted within atelecommunications network.

The computing environment 100 includes an optimum capacity solutionsystem 150 configured to monitor aspects of the network 130 based on,for example, data received from the network monitoring system. Theoptimum capacity solution system 150 can evaluate and select optimumnetwork performance improvement solutions to be deployed at cell sitesto improve their performance as described in detail below.

FIG. 1 and the discussion herein provide a brief, general description ofa suitable computing environment 100 in which the optimum capacitysolution system 150 can be supported and implemented. Although notrequired, aspects of the optimum capacity solution system 150 aredescribed in the general context of computer-executable instructions,such as routines executed by a computer, e.g., mobile device, a servercomputer, or personal computer. The system can be practiced with othercommunications, data processing, or computer system configurations,including: Internet appliances, hand-held devices (including tabletcomputers and/or personal digital assistants (PDAs)), Internet of Things(IoT) devices, all manner of cellular or mobile phones, multi-processorsystems, microprocessor-based or programmable consumer electronics,set-top boxes, network PCs, mini-computers, mainframe computers, and thelike. Indeed, the terms “computer,” “host,” and “host computer,” and“mobile device” and “handset” are generally used interchangeably herein,and refer to any of the above devices and systems, as well as any dataprocessor.

Aspects of the system can be embodied in a special purpose computingdevice or data processor that is specifically programmed, configured, orconstructed to perform one or more of the computer-executableinstructions explained in detail herein. Aspects of the system can alsobe practiced in distributed computing environments where tasks ormodules are performed by remote processing devices, which are linkedthrough any communications network, such as a Local Area Network (LAN),Wide Area Network (WAN), or the Internet. In a distributed computingenvironment, program modules can be located in both local and remotememory storage devices.

Aspects of the system can be stored or distributed on computer-readablemedia (e.g., physical and/or tangible non-transitory computer-readablestorage media), including magnetically or optically readable computerdiscs, hard-wired or preprogrammed chips (e.g., EEPROM semiconductorchips), nanotechnology memory, or other data storage media. Indeed,computer implemented instructions, data structures, screen displays, andother data under aspects of the system can be distributed over theInternet or over other networks (including wireless networks), on apropagated signal on a propagation medium (e.g., an electromagneticwave(s), a sound wave, etc.) over a period of time, or they can beprovided on any analog or digital network (packet switched, circuitswitched, or other scheme). Portions of the system reside on a servercomputer, while corresponding portions reside on a client computer suchas a mobile or portable device, and thus, while certain hardwareplatforms are described herein, aspects of the system are equallyapplicable to nodes on a network. In an alternative implementation, themobile device or portable device can represent the server portion, whilethe server can represent the client portion.

In some implementations, the user device 110 and/or the cell sites 120,125 can include network communication components that enable the devicesto communicate with remote servers or other portable electronic devicesby transmitting and receiving wireless signals using a licensed,semi-licensed, or unlicensed spectrum over communications network, suchas network 130. In some cases, the communication network 130 can becomprised of multiple networks, even multiple heterogeneous networks,such as one or more border networks, voice networks, broadband networks,service provider networks, Internet Service Provider (ISP) networks,and/or Public Switched Telephone Networks (PSTNs), interconnected viagateways operable to facilitate communications between and among thevarious networks. The telecommunications network 130 can also includethird-party communications networks such as a Global System for Mobile(GSM) mobile communications network, a code/time division multipleaccess (CDMA/TDMA) mobile communications network, a 3rd or 4thgeneration (3G/4G) mobile communications network (e.g., General PacketRadio Service (GPRS/EGPRS)), Enhanced Data rates for GSM Evolution(EDGE), Universal Mobile Telecommunications System (UMTS), or Long TermEvolution (LTE) network), 5G mobile communications network, IEEE 802.11(WiFi), or other communications networks. Thus, the user device isconfigured to operate and switch among multiple frequency bands forreceiving and/or transmitting data.

Further details regarding the operation and implementation of theoptimum capacity solution system 150 will now be described.

Examples of Identifying Optimum Network Performance ImprovementSolutions

FIG. 2 is a block diagram illustrating the components of the optimumcapacity solution system 150. The optimum capacity solution system 150can include functional modules that are implemented with a combinationof software (e.g., executable instructions, or computer code) andhardware (e.g., at least a memory and processor). Accordingly, as usedherein, in some examples a module is a processor-implemented module orset of code, and represents a computing device having a processor thatis at least temporarily configured and/or programmed by executableinstructions stored in memory to perform one or more of the specificfunctions described herein. For example, the optimum capacity solutionsystem 150 can include a historical data module 210, a clustering module220, a ranking module 230, and an optimum solution selection module 240,each of which is discussed separately below.

Historical Data Module

The historical data module 210 is configured and/or programmed togenerate/access/receive/collect a set of network improvement solutionsrecords for one or more locations over a period of time (e.g., daily,weekly, monthly, quarterly, yearly, etc.) (which can be stored in thecapacity solution database 255). The set of network improvementsolutions records comprises information about one or more of thefollowing parameters: location (e.g., latitude/longitude), market(s)associated with the location, network performance improvementsolution(s) deployed at the location, and an associated gain profile forthe deployed network performance improvement solutions. Additionally oralternatively, the set of network improvement solutions recordscomprises information about one or more of the following solutionsmetrics associated with the deployed network performance improvementsolutions: gain index, gain measures, time to deploy solution, lead timeto deploy solution, cost to deploy solution, cost to maintain solution,total cost of solution, expected lifetime of solution, average medianincome, user demographics (e.g., age, income, crime statistics,occupation, education level, ethnicity, and so on), duration of gain tocustomers, change in customers after deploying solution, change inrevenue after deploying solution, change in sales after deployingsolution, traffic, number of users, Physical Resource Block (PRB)utilization, Channel Quality Indicator (CQI), throughput, carrieraggregation, advanced Quadrature Amplitude Modulation (QAM), duration ofdeploying the network performance improvement solution, lifetime of thenetwork performance improvement solution, efficacy of the networkperformance improvement solution, location of the telecommunicationsnetwork site, lease information of the telecommunications network site,duration of deployment of the network performance improvement solution,entitlements and permits required to deploy the network performanceimprovement solution, tower height, nearest available site, populationserved by the telecommunications network site, households served by thetelecommunications network site, rent cost associated with the networkperformance improvement solution, backhaul availability, and so on. Theterm market is used to denote a geographic area, such as a portion (orall) of a city, town, state, country, other similar geographic construct(e.g., Pacific Northwest, Southeast, etc.), and so on. Each market canbe associated with a group of locations (e.g., latitude/longitudepairs).

U.S. Pat. No. 10,555,191, the contents of which are incorporated hereinin its entirety, describes methods and systems for computing gain metricvalues (gain measures) for network improvement solutions deployed at alocation. In some implementations, the historical data module 210collects capacity gain measure values for a set of key performanceindicators (KPIs) for both before and after deployment of networkimprovement solutions at locations. Examples of KPIs include, but arenot limited to traffic, number of users, PRB utilization, CQI, andthroughput. For example, the historical data module 210 collects threeyears of capacity gain measure values for the selected KPIs as well asthree years of each solution's lead time, cost for each market, andlocation. FIGS. 3A-3F are example of datagenerated/accessed/received/collected by the historical data module 210.

Clustering Module

The clustering module 220 is configured and/or programmed to generatemarket clusters for each solution to categorize solutions based on oneor more solutions metrics. In some implementations, the clusteringmodule 220 selects, for a market, a set of sites to consider forperforming the clustering analysis. For example, the clustering module220 selects the sites whose locations fall within the geographicboundaries of the market (e.g., North Seattle, Midtown New York City,Florida, etc.). Additionally, or alternatively, the clustering module220 can compute a score/priority for sites in the market using weightsbased on one or more of the following parameters: traffic, unique users,count of superphones (e.g., phone with certain characteristics such asrelease dates), revenue, throughput, duration of congestion, cost oflease/rent, median income, average household income, location of site,cost of hardware installed, age demographic distribution, and so on. Theparameter values can be of different time granularities, such as weekly,monthly, quarterly, one time, and so on. FIGS. 4A and 4B illustratescores generated for various sites in markets. The site scores can beused to rank the sites in the market, and the clustering module 220 canselect the top n ranked sites when performing the clustering analysisdiscussed below. In some implementations, the clustering module 220 canscore and rank sites in different markets using different parametersets.

After identifying the set of sites to consider for the clusteringanalysis, the clustering module 220 generates, for each market, clustersof network improvement solutions records for the market. The clusteringmodule can use techniques, such as k-means clustering, to generate theclusters. The clusters can be generated based on a group of locationsassociated with the market and/or values of one or more of the solutionmetrics discussed above. The clustering module 220 can select a subsetof the solution metrics discussed above based on solution metricsselection criteria, such as user selection, output optimization criteria(e.g., expected gain, length of solution to deploy, best solution,desired location characteristics, and so on), correlation betweensolution metric and efficacy of solution/gain measures, top n KPIs, andso on.

FIGS. 6A-6B illustrate examples of generating six clusters, along withseveral cluster attributes (e.g., gain, lead time, and best solution)for a set of network improvement solutions records associated with ageographic area 605.

Ranking Module

The ranking module 230 is configured and/or programmed to rank clustersand/or network performance improvement solutions in the generatedclusters based on one or more of the following cluster rankingparameters: spectrum, duration, location, gain, cost to deploy solution,and so on. FIG. 6B illustrates a table 610 depicting a set of clustersassociated with a geographic area, ranked according to their respectivegains.

Optimum Solution Selection and Ranking Module

The optimum solution selection and ranking module 240 is configuredand/or programmed to select and recommend one (or more) networkperformance improvement solutions to deploy at particularlocations/sites. Examples of network performance improvement solutionsinclude, but are not limited to adding spectrum, removing spectrum,adding a proximate cell site, removing a proximate cell site, displacinga proximate cell site, adding or enhancing at least one technologycapability, cell split, small cell deployment, sector addition, sectorremoval, sector capacity enhancement, cell on wheels addition, cell onwheel removal, tower addition, tower removal, hot spots addition, hotspots removal, capacity modification, and so on.

The optimum solution selection and ranking module 240 receives acandidate location at which a user desires to deploy network performanceimprovement solution(s). For example, the optimum capacity solutionsystem 150 can receive a user selection of a location via a userinterface (e.g., a user can enter a location name, latitude/longitude,click on a location on a map, etc.). Upon receiving a desired locationdetails, the optimum solution selection and ranking module 240identifies a prioritized set of network performance improvementsolutions capable of being deployed at the received location. Theoptimum solution selection and ranking module 240 identifies theprioritized set of network performance improvement solutions based onvalues of a set of prioritization parameters and the generated clusters.The set of prioritization parameters comprises one or more of thefollowing: gain index, gain measures, time to deploy solution, lead timeto deploy solution, cost to deploy solution, cost to maintain solution,total cost of solution, expected lifetime of solution, average medianincome, user demographics (e.g., age, income, crime statistics,occupation, education level, ethnicity, and so on), duration of gain tocustomers, change in customers after deploying solution, change inrevenue after deploying solution, change in sales after deployingsolution, traffic, number of users, Physical Resource Block (PRB)utilization, Channel Quality Indicator (CQI), throughput, carrieraggregation, advanced Quadrature Amplitude Modulation (QAM), duration ofdeploying the network performance improvement solution, lifetime of thenetwork performance improvement solution, efficacy of the networkperformance improvement solution, location of the telecommunicationsnetwork site, lease information of the telecommunications network site,duration of deployment of the network performance improvement solution,entitlements and permits required to deploy the network performanceimprovement solution, tower height, nearest available site, populationserved by the telecommunications network site, households served by thetelecommunications network site, rent cost associated with the networkperformance improvement solution, backhaul availability, and so on.Alternatively or additionally, the optimum solution selection andranking module 240 receives one or more prioritization parameters asoutput metrics that are to be optimized at the desired location, such asexpected gain, length of solution to deploy, best solution, and so on.

To identify the prioritized set of network performance improvementsolutions, the optimum solution selection and ranking module 240identifies one or more candidate markets such that a group of locationsassociated with the candidate market are closest to the candidatelocation in light of the received prioritization parameters. Closenessbetween the group of locations associated with a candidate market andthe candidate location can be determined based on closeness metrics,such as geographic distance, similarity between locationcharacteristics, and so on. For example, similarity between locationcharacteristics can be determined based on location similarity factors,such as demographics of users associated with a location, location type(e.g., urban, suburban, rural, etc.), legal regulations associated withthe location, location coverage area, points of interest at thelocation, and so on.

As an example, the optimum capacity solution system 150 receives acandidate location selection from a user (e.g., when a user enters alatitude/longitude of a location and/or selects a desired location on amap). Upon receiving the candidate location, the optimum solutionselection and ranking module 240 identifies one or more clusters basedon the following characteristics: nearest site with previous solution,gains measured, time to deploy solution, cost of solution, bestsolution, and other measured KPIs. Then, the optimum solution selectionand ranking module 240 can predict based on the ranked solutions in theidentified clusters (which are based on historical data), a prioritizedset of network performance improvement solutions (e.g., comprising thetop n solutions in an identified cluster) and their expected gain, timeto deploy solution, and so on.

Additionally or alternatively, the optimum solution selection andranking module 240 computes a rank value for each network performanceimprovement solution in the prioritized set of network performanceimprovement solutions based on the values of the set of prioritizationparameters. The optimum solution selection and ranking module 240 canthen select and/or implement, at the candidate location, an optimumnetwork performance improvement solution from the prioritized set ofnetwork performance improvement solutions based on the computed rankvalues.

Flow Diagrams

FIG. 5A is a flow diagram illustrating a process 500 of identifyingoptimum network performance improvement solution at a location in atelecommunications network. Process 500 begins at block 505 where itaccesses a set of network improvement solutions records for multiplelocations. Each record in the set of network improvement solutionsrecords comprises information about a location, at least one networkperformance improvement solution deployed at the location, and anassociated gain profile for the at least one network performanceimprovement solution. At blocks 510-520, for each market in a set ofmarkets, wherein each market is associated with a group of locations,process 500: generates a cluster of network improvement solutionsrecords for the market based on the group of locations associated withthe market and solutions metric values associated with networkperformance improvement solutions deployed at each of the group oflocations associated with the market (block 510); and ranks networkperformance improvement solutions in the cluster based on, for example,one or more of the following cluster ranking parameters: spectrum,duration, location, and cost to deploy solution (block 515). In someimplementations, process 500 ranks the clusters for a market based onone or more metrics, such as gain, lead time, best solution, and so on.

At block 525, process 500 receives a candidate location to identify aprioritized set of network performance improvement solutions capable ofbeing deployed at the candidate location. At block 530, process 500receives a set of prioritization parameters. Using the candidatelocation and the set of prioritization parameters, at block 535, process500 identifies a prioritized set of network performance improvementsolutions capable of being deployed at the candidate location. At block540, process 500 computes a rank value for each network performanceimprovement solution in the prioritized set of network performanceimprovement solutions based on, for example, the values of the set ofprioritization parameters. Finally, at block 545, process 500 selectsand/or implements, at the candidate location, an optimum networkperformance improvement solution selected from the prioritized set ofnetwork performance improvement solutions based on the computed rankvalues.

FIG. 5B is a flow diagram illustrating a process 550 of identifyingoptimum network performance improvement solution at a location in atelecommunications network.

CONCLUSION

Unless the context clearly requires otherwise, throughout thedescription and the claims, the words “comprise,” “comprising,” and thelike are to be construed in an inclusive sense, as opposed to anexclusive or exhaustive sense; that is to say, in the sense of“including, but not limited to.” As used herein, the terms “connected,”“coupled,” or any variant thereof, means any connection or coupling,either direct or indirect, between two or more elements; the coupling ofconnection between the elements can be physical, logical, or acombination thereof. Additionally, the words “herein,” “above,” “below,”and words of similar import, when used in this application, shall referto this application as a whole and not to any particular portions ofthis application. Where the context permits, words in the above DetailedDescription using the singular or plural number can also include theplural or singular number respectively. The word “or,” in reference to alist of two or more items, covers all of the following interpretationsof the word: any of the items in the list, all of the items in the list,and any combination of the items in the list.

The above detailed description of implementations of the system is notintended to be exhaustive or to limit the system to the precise formdisclosed above. While specific implementations of, and examples for,the system are described above for illustrative purposes, variousequivalent modifications are possible within the scope of the system, asthose skilled in the relevant art will recognize. For example, somenetwork elements are described herein as performing certain functions.Those functions could be performed by other elements in the same ordiffering networks, which could reduce the number of network elements.Alternatively, or additionally, network elements performing thosefunctions could be replaced by two or more elements to perform portionsof those functions. In addition, while processes, message/data flows, orblocks are presented in a given order, alternative implementations canperform routines having blocks, or employ systems having blocks, in adifferent order, and some processes or blocks can be deleted, moved,added, subdivided, combined, and/or modified to provide alternative orsubcombinations. Each of these processes, message/data flows, or blockscan be implemented in a variety of different ways. Also, while processesor blocks are at times shown as being performed in series, theseprocesses or blocks can instead be performed in parallel, or can beperformed at different times. Further, any specific numbers noted hereinare only examples: alternative implementations can employ differingvalues or ranges.

The teachings of the methods and system provided herein can be appliedto other systems, not necessarily the system described above. Theelements, blocks and acts of the various implementations described abovecan be combined to provide further implementations.

Any patents and applications and other references noted above, includingany that can be listed in accompanying filing papers, are incorporatedherein by reference. Aspects of the technology can be modified, ifnecessary, to employ the systems, functions, and concepts of the variousreferences described above to provide yet further implementations of thetechnology.

These and other changes can be made to the invention in light of theabove Detailed Description. While the above description describescertain implementations of the technology, and describes the best modecontemplated, no matter how detailed the above appears in text, theinvention can be practiced in many ways. Details of the system can varyconsiderably in its implementation details, while still beingencompassed by the technology disclosed herein. As noted above,particular terminology used when describing certain features or aspectsof the technology should not be taken to imply that the terminology isbeing redefined herein to be restricted to any specific characteristics,features, or aspects of the technology with which that terminology isassociated. In general, the terms used in the following claims shouldnot be construed to limit the invention to the specific implementationsdisclosed in the specification, unless the above Detailed Descriptionsection explicitly defines such terms. Accordingly, the actual scope ofthe invention encompasses not only the disclosed implementations, butalso all equivalent ways of practicing or implementing the inventionunder the claims.

While certain aspects of the technology are presented below in certainclaim forms, the inventors contemplate the various aspects of thetechnology in any number of claim forms. For example, while only oneaspect of the invention is recited as implemented in a computer-readablemedium, other aspects can likewise be implemented in a computer-readablemedium. Accordingly, the inventors reserve the right to add additionalclaims after filing the application to pursue such additional claimforms for other aspects of the technology.

1. An apparatus to identify network improvement solutions deployable atone or more locations in a wireless telecommunications network, theapparatus comprising: at least one data processor; and at least onememory, communicatively coupled to the at least one data processor, andstoring instructions executable by the at least one data processor,wherein the instructions comprise: using historical data to train amachine learning model, wherein the historical data includes capacitygain solutions, and associated gains achieved based on the solutions,and wherein the capacity gain solutions were previously implemented atvarious geographic locations associated with the wirelesstelecommunications network; upon receiving a new geographic location,generating, with the trained machine learning model, recommendedsolutions deployable at the new location to improve performance of thewireless telecommunications network at the new location, wherein themachine learning model uses clustering techniques to perform therecommendations.
 2. The apparatus of claim 1, wherein the instructionsfurther comprise: building or accessing a dataset comprising informationof previously deployed capacity improvement solutions within thewireless telecommunications network, wherein the information includesdata related to gain, cost, location, duration, and solution type. 3.The apparatus of claim 2, wherein the clustering includes clustering onthe information in the dataset for each recommended solution, andcategorizing each recommended solution within a geographic area.
 4. Theapparatus of claim 1, wherein the clustering includes ranking eachrecommended solution in each cluster based on one or more of: spectrum,duration, area, or cost.
 5. The apparatus of claim 1, wherein theinstructions further comprise: receiving a user input of a newgeographic location; finding a nearest cluster or recommended solutions,and outputting a top n number of recommended solutions for the nearestcluster.
 6. The apparatus of claim 1, further comprising receiving inputfrom a user clicking on a location on a displayed map to identify thenew geographic location.
 7. At least one computer-readable medium,excluding transitory signals and carrying instructions, which whenexecuted by a data processor, performs operations for a wirelesstelecommunications network, the operations comprising: using historicaldata to train a machine learning model, wherein the historical dataincludes capacity gain solutions, and gains achieved based on thesolutions, and wherein the capacity gain solutions were previouslyimplemented at multiple geographic locations associated with thewireless telecommunications network; and, upon receiving a newgeographic location, generating, with the trained machine learningmodel, recommended solutions deployable at the new location to improveperformance of a wireless telecommunications network at the newlocation, wherein the machine learning model uses clustering techniquesto perform the recommendations.
 8. The at least one computer-readablemedium of claim 7, wherein the instructions further comprise: buildingor accessing a dataset comprising information of previously deployedcapacity improvement solutions within the wireless telecommunicationsnetwork, wherein the information includes data related to at least threeof: gain, cost, location, duration, or solution type.
 9. The at leastone computer-readable medium of claim 8, wherein the clustering includesclustering on the information in the dataset for each recommendedsolution, and categorizing each recommended solution within a geographicarea.
 10. The at least one computer-readable medium of claim 7, whereinthe clustering includes ranking each recommended solution in eachcluster based on one or more of: spectrum, duration, area, or cost. 11.The at least one computer-readable medium of claim 7, wherein theinstructions further comprise: receiving a user input of the newgeographic location; finding a nearest cluster or recommended solutions,and outputting a top n number of recommended solutions for the nearestcluster.
 12. The at least one computer-readable medium of claim 7,further comprising receiving input from a user clicking on a location ona displayed map.
 13. At least one computer-readable medium, excludingtransitory signals and carrying instructions, which when executed by adata processor, performs operations, comprising: receiving data relatedto existing capacity planning solutions deployed at multiple existinglocations; training a machine learning model based on the existingcapacity planning solutions data, wherein training the machine learningmodel includes creating data clusters based on the existing capacityplanning solutions data; receiving latitude and longitude coordinates ofa new geographic location; and applying classification techniques todetermine optimum capacity planning solutions capable of being deployedat the new geographic location using the created data clusters, tothereby efficiently and economically identify solutions and locations toexpand capacity of cell sites at the new location within a wirelesstelecommunications network and improve telecommunications networkperformance.
 14. The at least one computer-readable medium of claim 13,wherein receiving data includes receive historical data having capacitygain solutions and associated gains at the multiple existing locations;and wherein the applying includes recommending, using the trainedmachine learning model, a top n solutions to be deployed at the newlocation.
 15. The at least one computer-readable medium of claim 13,further comprising: building or accessing a dataset comprising theexisting capacity planning solutions data, wherein the existing capacityplanning solutions data includes at least two of: gain, cost, location,duration, or solution type.
 16. The at least one computer-readablemedium of claim 13, further comprising: clustering the existing capacityplanning solutions data per market for each of multiple solutions tocategorize solutions within a geographic area.
 17. The at least onecomputer-readable medium of claim 13, further comprising: ranking thesolutions in each data cluster based on: spectrum, duration,latitude/longitude area, cost, or any combination thereof.
 18. The atleast one computer-readable medium of claim 13, further comprising:receiving user input of the new location via entering of the latitude orlongitude or by clicking on a location on a map.
 19. The at least onecomputer-readable medium of claim 13, wherein the applying includesfinding a nearest cluster and showing a top n solutions in the nearestcluster.